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By synergistically integrating mobile networks and embodied artificial intelligence (AI), Mobile Embodied AI Networks (MEANETs) represent an advanced paradigm that facilitates autonomous, context-aware, and interactive behaviors within dynamic environments. Nevertheless, the rapid development of MEANETs is accompanied by challenges in trustworthiness and operational efficiency. Fortunately, blockchain technology, with its decentralized and immutable characteristics, offers promising solutions for MEANETs. However, existing block propagation mechanisms suffer from challenges such as low propagation efficiency and weak security for block propagation, which results in delayed transmission of vehicular messages or vulnerability to malicious tampering, potentially causing severe traffic accidents in blockchain-enabled MEANETs. Moreover, current block propagation strategies cannot effectively adapt to real-time changes of dynamic topology in MEANETs. Therefore, in this paper, we propose a graph Resfusion model-based trustworthy block propagation optimization framework for consortium blockchain-enabled MEANETs. Specifically, we propose an innovative trust calculation mechanism based on the trust cloud model, which comprehensively accounts for randomness and fuzziness in the miner trust evaluation. Furthermore, by leveraging the strengths of graph neural networks and diffusion models, we develop a graph Resfusion model to effectively and adaptively generate the optimal block propagation trajectory. Simulation results demonstrate that the proposed model outperforms other routing mechanisms in terms of block propagation efficiency and trustworthiness. Additionally, the results highlight its strong adaptability to dynamic environments, making it particularly suitable for rapidly changing MEANETs., Comment: 15 pages, 11 figures

Tumor Treating Fields (TTFields) is a non-invasive anticancer modality that utilizes alternating electric fields to disrupt cancer cell division and growth. While generally well-tolerated with minimal side effects, traditional TTFields therapy for lung tumors faces challenges due to the influence of respiratory motion. We design a novel closed-loop TTFields strategy for lung tumors by incorporating electrical impedance tomography (EIT) for real-time respiratory phase monitoring and dynamic parameter adjustments. Furthermore, we conduct theoretical analysis to evaluate the performance of the proposed method using the lung motion model. Compared to conventional TTFields settings, we observed that variations in the electrical conductivity of lung during different respiratory phases led to a decrease in the average electric field intensity within lung tumors, transitioning from end-expiratory (1.08 V/cm) to end-inspiratory (0.87 V/cm) phases. Utilizing our proposed closed-Loop TTFields approach at the same dose setting (2400 mA, consistent with the traditional TTFields setting), we can achieve a higher and consistent average electric field strength at the tumor site (1.30 V/cm) across different respiratory stages. Our proposed closed-loop TTFields method has the potential to improved lung tumor therapy by mitigating the impact of respiratory motion., Comment: 7 pages, 5 figures

We report a digital twin (DT) framework of electrical tomography (ET) to address the challenge of real-time quantitative multiphase flow imaging based on non-invasive and non-radioactive technologies. Multiphase flow is ubiquitous in nature, industry, and research. Accurate flow imaging is the key to understanding this complex phenomenon. Existing non-radioactive multiphase flow imaging methods based on electrical tomography are limited to providing qualitative images. The proposed DT framework, building upon a synergistic integration of 3D field coupling simulation, model-based deep learning, and edge computing, allows ET to dynamically learn the flow features in the virtual space and implement the model in the physical system, thus providing unprecedented resolution and accuracy. The DT framework is demonstrated on gas-liquid two-phase flow and electrical capacitance tomography (ECT). It can be readily extended to various tomography modalities, scenarios, and scales in biomedical, energy, and aerospace applications as an effective alternative to radioactive solutions for precise flow visualization and characterization.

Electrical capacitance tomography (ECT) has been investigated in many fields due to its advantages of being non-invasive and low cost. Sparse algorithms with l1-norm regularization are used to reduce the smoothing effect and obtain sharp images, such as total variation (TV)regularization. This paper proposed for the first time to solve the ECT inverse problem using an l0-norm regularization algorithm, namely the doubly extrapolated proximal iterative hard thresholding (DEPIHT) algorithm. The accelerated alternating direction method of multipliers (AADMM) algorithm, based on the TV regularization, has been selected to acquire the first point for the DEPIHT algorithm. Experimental tests were carried out to validate the feasibility of the AADMM-DEPIHT algorithm,which is compared with the Landweber iteration (LI) and AADMM algorithms. The results show the AADMM-DEPIHT algorithm has an improvement on the quality of images and also indicates that the DEPIHT algorithm can be a suitable candidate for ECT in post-process.